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It has more computational capabilities than the previously discussed single layer neural network. Apart from single-input layer and single-output layer, it has one or more hidden layers depending on the complexity of the computational problem. Unlike to single-layer network, multi-layer network can learn non-linear functions. Computations on the weighted inputs are performed in the hidden layers and generate the net input on which activation functions are applied to produce the actual output.

Figure 2.7 Single layer neural network.

Figure 2.8 Multilayer neural network.

Figure 2.8 represents a model of fully connected multi-layer neural network. In this structure, the input layer shown by yellow nodes receive the inputs x₁, x₂, x₃, … …, xN and pass it to the first hidden layer, represented by gray nodes in Figure 2.8. If the model has multiple hidden layers, then the output from the first hidden layer is passed to the next hidden layer and so on. Finally, when the output from the last hidden layer reaches to the output layer represented by the green nodes, it produces the final output y₁, y₂, y₃, … …, yN. It can be said that the multi-layer network consists of a number of single layer network arrange in a cascading manner.